Orientation-Aided Stochastic Magnetic Matching for Indoor Localization

Indoor localization using magnetic and inertial sensors has shown its effectiveness due to the pervasiveness of magnetic fields and independence from external infrastructure. However, when massive crowdsourced data are obtained in mass-market applications, new issues occur and degrade the magnetic matching (MM) performance. To alleviate these issues, an orientation-aided stochastic MM method is presented. First, orientation-aided magnetic fingerprints are applied to effectively improve fingerprint fidelity when massive data are used. Second, this paper characterizes crowdsourced magnetic fingerprints and reveals that the stochastic magnetic components may have different histograms, such as the symmetric, left-skewed, bimodal, and irregular ones. Based on such outcome, Gaussian distribution and histogram model based stochastic MM methods are presented to mitigate the degradation from stochastic magnetic components, which have not been involved in the existing MM methods such as nearest neighbor and dynamic time warping. Compared to the deterministic MM methods, this research provides a deeper insight into the use of magnetic fingerprints and more accurate MM and MM/dead-reckoning integrated localization solutions.